Update app.py

app.py

@@ -1,63 +1,40 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from mistralai import Mistral, UserMessage
+from dotenv import load_dotenv, find_dotenv
+
+
+def chat_bot(user_input):
+    load_dotenv(find_dotenv())
+    miss_api_key = os.environ["MISTRAL_API"]
+    model = "mistral-small-latest"
+
+    client = Mistral(api_key=miss_api_key)
+
+    message = [{
+        "role": "user",
+        "content": user_input,
+    }, ]
+
+    chat_response = client.chat.complete(
+        model=model,
+        messages=message,
+    )
+    # response = client.create_chat_completion()
+    return chat_response.choices[0].message.content
+
+    # for chunk in client.chat.stream(model=model, messages=message):
+    # if "delta" in chunk.data.choices[0]:
+    #   if chunk.data.choices[0].delta.content:
+    #      print(chunk.data.choices[0].delta.content, end="")
+
+
+iface = gr.Interface(
+    fn=chat_bot,
+    inputs="text",
+    outputs="text",
+    description="Guides the website usage",
+    examples=[["How can you help me?"]],
+    title="Website Guide"
 )
-
-
-if __name__ == "__main__":
-    demo.launch()
+iface.launch()